Enhanced Face Recognition System Using Age Invariant Features

Automatic face recognition system poses a various difficult problem using different age faces. Mostly face recognitions have addressed variation in ages problems like age simulation or age estimation. Age invariant is a complex procedure that influences both face texture and the shape of face. In automatic face recognition system these texture and shape changes degrade the performance. The problem is designing an effective matching framework of face recognition system using different images of aging. In this paper to develop a model for automatic age invariant face recognition model using facial features. In the model, extract the facial features like eye, mouth and nose in the given image based on the illumination method. Once the eye, nose, and mouth are detected, need for aligning the face. The face alignment depends on the angle between the eye and mouth coordinates. Once the face alignment completed, calculate the eigenvalue and eigenvectors of co-variance matrix. Then find difference between all features to get a difference vector. The eigenfaces calculates the eigenvalues and eigenvectors. Compare these distance vectors for all faces to get a most match value or minimum mismatch value. Give the face with the most matches or minimum mismatch value as the output. The results of various experiments shows that this method outperforms the usage of the face aging data sets of IFDA. A fusion of age invariant face recognition using facial features model further improves the accuracy of the face matching in the presence of aging.